Cybernetics and Computer Engineering, 2019, 2 (196), pp. 3-26
Fainzilberg L.S., DSc. (Engineering), Professor,
Chief Researcher of the Department of Intelligent Automatic Systems
International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine,
Acad. Glushkov av., 40, Kiev, 03187, Ukraine
Dykach Ju.R.2, Student, Faculty of Biomedical Engineering,
The National Technical University of Ukraine
«Igor Sikorsky Kyiv Polytechnic Institute»,
37, Peremohy av., Kyiv, 03056, Ukraine
LINGUISTIC APPROACH FOR ESTIMATION OF ELECTROCARDIOGRAMS’S SUBTLE CHANGES BASED ON THE LEVENSTEIN DISTANCE
Introduction. The linguistic approach, based on the transition from electrocardiogram (ECG) to codogram, gained fame for the analysis of heart rhythm. To expand the functionality of the method, it is advisable to study the possibility of simultaneously monitoring the dynamics of changes in the duration of cardiac cycles and the indicator of symmetry T-wave that carries information about ischemic changes in the myocardium.
The purpose of the article is to develop algorithmic and software components to solve this problem and conduct experimental studies on model and real data.
Methods. ECG of certain groups was automatically encoded, Levenshtein distance was calculated between ECG pairs for group and the reference codogram of the group was constructed. The evaluation of the results of experimental studies was carried out on the basis of traditional methods of statistical analysis.
Results. It is shown that based on the Levenshtein distance between all pairs of codograms of the test group, the reference codogram of the group of patients with coronary heart disease (CHD) and the group of healthy volunteers can be determined. It was established that making decisions based on the comparison of the ECG codogram of the person with the reference codogram allows for the separation of representatives of the indicated groups with sensitivity SE = 72% and specificity CP = 79% even in those cases when the traditional analysis of the ECG in 12 leads is not informative.
Conclusions. The proposed approach allows to obtain additional diagnostic information when solving actual problems of practical medicine.
Keywords: linguistic approach, diagnostic sign of ECG, Levenshtein distance.
1 Kanjilal P. P., Bhattacharya J., Saga G. Robust Method for Periodicity Detection and Characterisation of Irregular Cyclical Series in Terms of Embedded Periodic Components. Phys. Rev. 1999. Vol. 59. P. 4013-4025. https://doi.org/10.1103/PhysRevE.59.4013
2 Fainzilberg L.S.Generalized Method of Processing Cyclic Signals of Complex Form in Multidimension Space of Patameters. Journal of Automation and Information Sciences. 2015. Vol. 47. Issue 3. P. 24-39. DOI: 10.1615/JAutomatInfScien.v47.i3.30 https://doi.org/10.1615/JAutomatInfScien.v47.i3.30
3 Connolly D.C., Elveback L.R., Oxman H.A. Coronary heart disease in residents of Rochester, Minnesota: Prognostic value of the resting electrocardiogram at the time of initial diagnosis of angina pectoris. Mayo.Clin.Proc. 1984. Vol. 59. P. 247-250. https://doi.org/10.1016/S0025-6196(12)61257-9
4 Solopov V.N., Sadykova A.R., Fedoseeva T.S. Limitations of automatic computerized analysis of an electrocardiogram. Kazan Medical Journal. 2012. Vol. 93. No. 4. P. 687-691. (In Russian).
5 Pavlidis T. Linguistic analysis of waveforms. Software Eng. 1971. Vol. 2. No. 4. P. 203-225. – https://doi.org/10.1016/B978-0-12-696202-4.50019-X.
6 Mottl N.V., Muchnik I.B., Jakovled V.G. Optimal segmentation of experimental curves. Automation and remote control. 1983. Issue 8. P. 84-95. (In Russian).
7 Haralick R.M. Structural pattern recognition homomorphisms and arrangements. Pattern recognition. 1978. Vol. 10. No. 3. P.223-236. https://doi.org/10.1016/0031-3203(78)90030-4
8 Goldberger A.L. Fractal mechanisms in the electrophysiology of the heart. IEEE Eng. Med. Biol. 1992. No 11. P. 47-52. https://doi.org/10.1109/51.139036
9 Cherkay A.D., Vlasov Ju.A. Linguistic analysis of the sequence of time intervals between heartbeats. In Theory and practice of automation of electrocardiographic and clinical research. Kaunas: Kaunas Medical Institute. 1977. P. 128-131. (In Russian)
10 Maksimov A.V. ECG signals analyzing using translating grammars. In News of the Taganrog University of Radio Engineering. Thematic issue “Computer technology in engineering and management practice”. 2001. P. 210-216. (In Russian).
11 Tsidypov Ch.S. Pulse diagnosis of Tibetan medicine. Novosibirsk: Science, 1988. 134 p. (In Russian).
12 Bajevsky R.M., Ivanov G.G. Heart rate variability: theoretical aspects and possibilities of clinical use. Ultrasound and functional diagnostics. 2001. No 3. P C. 108-127. (In Russian).
13 McCrafy R. New Frontiers in Heart Rate Variability and Social Coherence Research: Techniques, Technologies, and Implications for Improving Group Dynamics and Outcomes. Front Public Health. 2017. No. 5. P. 2-13. https:// doi:10.3389/fpubh.2017.00267. https://doi.org/10.3389/fpubh.2017.00267
14 Uspenskiy V.M. Diagnostic System Based on the Information Analysis of Electrocardiogram. Proceedings of MECO 2012. Advances and Challenges in Embedded Computing (Montenegro, June 19-21). 2012. P. 74-76.
15 Kolesnikova O.V., Krivenko S.S. Information analysis of electrocardiosignals: justification and possibilities. Papers of scientific works of the First International Scientific and Practical Conference “Information Systems and Technologies in Medicine” (ISM-2018). Kharkiv: KNURE. 2018. P. 161-163. (In Ukrainian)
16 Fainzilberg L.S. The basic of fasegraphy. Kyiv: Osvita Ukraini, 2017. 264 p. (In Russian).
17 Fainzilberg L.S. Computer diagnostics based on the electrocardiogram phase portrait. Kyiv: Osvita Ukraini, 2013. 191 p. (In Russian).
18 Fainzilberg L.S. ECG Averaging based on Hausdorff Metric. International Journal of Biomagnetism. 2003. Vol. 5. No 1. P. 236-237.
19 Fainzilberg L.S. Intellectual possibilities and prospects for the development of fasegraph information technology of processing signals of complex shape. Kibernetika i vycislitel’naa tehnika. 2016. Issue 186. P. 56-77. (In Russian). https://doi.org/10.15407/usim.2018.03.003
20 Dyachuk, D.D., Gritsenko, V.I., Fainzillberg, L.S. 2017. Application of a method of a phasagraphy during the screening of ischemic heart disease. Methodical recommendations of the Ministry of Health of Ukraine No. 163.16/13.17, Kyiv: Ukrainian Center for Scientific Medical Information and Patent and Licensing. 32 p. (In Ukrainian).
21 Gonzalez R.C. Syntactic pattern recognition. Introduction and survey. Proceedings of the National Electronic Conference. 1972. Vol. 27. No. 1. P. 27-32.
22 Braverman E.M., Muchnic I.B. Structural methods for processing empirical data. Moscow: Nauka. 1983. 492 p. (In Russian).
23 Biermann A.W. A Grammatical inference program for linear languages. International Conference of System Science (Hawall, 1971). 1971. Vol. S1. P. 117-141.
24 Levenshtein V.I. Binary codes with corrections for fallouts, insertions and substitutions of characters. Reports of the USSR Academy of Sciences. 1965. Vol. 163. No. 4. P. 845-848. (In Russian).
25 Wagner R.A., Fischer M.J. The String-to-String Correction Problem. Journal of the ACM. 1971. Vol. 21. Issue 1. P. 168-173. https:// doi: 10.1145/321796.321811 https://doi.org/10.1145/321796.321811
26 Fainzilberg L.S. Simulation models of artificial electrocardiogram generation under internal and external disturbances conditions. Journal of Qafgaz University. Mathematics and Computer Science. 2012. No. 34. P. 92-104. (In Russian).
27 Fainzilberg L.S., Orikhovska K.B. Information technology of the organism adaptation reserves assessment in field conditions. Kibernetika i vycislitel’naa tehnika. 2015. Issue 181. P. 4-22. (In Russian). https://doi.org/10.15407/kvt181.01.005
28 Gornan I.I. Probability theory and mathematical statistics for researchers and engineers. Kyiv: IPMMS NAS of Ukraine, 2003. 244 p. (In Ukrainian).
29 Schijvenaars B.J.A, Van Herpen G., Kors J.A. Intraindividual variability in electrocardiograms. Journal of Electrocardiology. 2008. Vol. 41. Issue 3. P. 190-196. doi: 10.1016/j.jelectrocard.2008.01.012 https://doi.org/10.1016/j.jelectrocard.2008.01.012
30 Fainzilberg L.S. Guaranteed estimate of the features utility while statistical recognition of two classes. Journal of Automation and Information Sciences. 2010. Vol. 42. Issue 10. P. 32-48. https://doi.org/10.1615/JAutomatInfScien.v42.i10.40